Learning & development departments are at a crossroads. Staffs are stretched to the limit creating, vetting, evaluating, and maintaining content, programs, and curriculum, while looking forward to the future and trying out new things. No consistent or repeatable frameworks exist to test and review emerging technologies such as augmented reality, blockchain, IoT or voice, or AI.

Let's face it, auto generated online certification has no real credibility, as you cannot guarantee if the owner of the certificate is the actual person who passed the course. Also, certificates can easily be reproduced by anyone with some Photoshop skills who has access to the template or a sample. Currently, facial recognition is the most popular biometric technology on the market. It combines high accuracy and high speed of operation. Biometrics is the only form of identification that truly identifies a person. Also called 1:1 comparison, authentication answers questions like: Are you the person you pretend to be? Are you in our database? And do you have the right credentials? In contrast to passwords, PINs, cards, and other forms of identifiers, biometric ID can't be lost, forgotten, or stolen.

Many L&D organizations are struggling with how to evaluate virtual and other immersive technologies for their learning and training initiatives. Options abound, and with a lot of pressure on corporate training organizations to include VR in their training, today's learning leaders need help in choosing the right solution. A successful evaluation process includes: 1) understanding of how to establish organizational and learner needs criteria to evaluate the immersive technology options, 2) knowing the strengths of the different approaches from both a cognitive science and technical perspective, and 3) recognizing the impacts of how the technology is brought into the organization. The best way to illustrate this process is through hands-on examples in diverse learning situations.

Making the workforce future proof is currently high on the agenda of all of us in training and education. In this session you will get an idea of how InnoEnergy uses artificial intelligence (AI) to bridge the skills and competency gap of engineers working in the sustainable energy field. You will learn the three steps that make the full cycle of the project: 1) Extracting emerging skills and competencies from industry reports and road maps, 2) Analyzing resumes of engineers who want to enter the energy sector or want to upskill or reskill their capabilities, and 3) Using the skills gap that emerges from the first two steps to come to an adaptive learning path in the third step of the process.  

Our work on competency-based medical education helped us create a learner-centered model that de-emphasized equating competency with grades and focused instead on abilities and achievement as salient learning outcomes. A challenge to this model, though, was providing verification of skill acquisition in an appropriate format. Through a year-long journey we were able to develop a blockchain-based credential framework that could help us execute on this model, and the lessons that we learned could easily apply to other industries as well.

People are becoming used to increasing levels of digital sophistication in concierge chatbots like Alexa or Google Assistant. As a result, they’ve truly started to embrace the concept of chatting or talking with these intelligent systems. Our audiences are incredibly busy, can quickly get frustrated when using an LMS, and don't often know answers to common questions such as what courses they should take (or which they started and forgot about), or where to locate content. Concierge chatbots could easily provide these answers through a single, familiar interface, while also increasing engagement and digital respect for L&D.

Artificial intelligence (AI) is a popular topic as of late, but what does it really mean for L&D? Understanding the potential use cases of AI for learning and development is critical to being a savvy consumer. It's not about the technology itself, but the potential it has for solving difficult organizational challenges.

Automation is no longer a word that you can ignore. While it may seem reserved for manual labor and robotics, it's actually impacting every industry and market. L&D is not immune. With the constant push to do things faster, better, and cheaper, it can be felt in delivering the products and services that L&D provides. Because of that, leveraging process and task automation may become an important way to help your team remain relevant. L&D automation can take many forms, but how do you start? Since automation for the sake of automation can be destructive and cause additional friction, how do you make strategic choices about which situations it’s best suited for?